Through an algorithm called counterfactual regret minimization, it began by playing at random, and eventually, after several months of training and trillions of hands of poker, it too reached a level where it could not just challenge the best humans but play in ways they couldn't—playing a much wider range of bets and randomizing these bets, so that rivals have more trouble guessing what cards it holds. "We give the AI a description of the game. We don't tell it how to play," says Noam Brown, a CMU grad student who built the system alongside his professor, Tuomas Sandholm. "It develops a strategy completely independently from human play, and it can be very different from the way humans play the game."